Fraud detection in electric energy using differential evolution

Angelo Darcy Molin Brun, João Onofre Pereira Pinto, Alexandra Maria Almeira Carvalho Pinto, Leandro Sauer, Evando Colman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

This work proposes the use of deferential evolution algorithm to find the parameters of a data mining system used to pre-select electrical energy consumers with suspect of fraud. A pattern recognition system was built in order to identify suspicious behavior of electrical energy consumers. However, the system only indicates such clients, and the frauds must be confirmed through in locus inspection. For that reason, it is important that true alarms be high to justify the trade-off of the inlocus inspection. Therefore, the parameter of the pattern recognition system must be well tuned, and that can be modeled as an optimization problem using the available training data. This work describes the pattern recognition system in details, and shows the algorithm modeling as an optimization problem. The defferential algorithm will be described and results will be show. Results confirm that this approach is feasible.

Original languageEnglish
Title of host publication2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09
DOIs
StatePublished - Dec 9 2009
Event2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09 - Curitiba, Brazil
Duration: Nov 8 2009Nov 12 2009

Publication series

Name2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09

Conference

Conference2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09
Country/TerritoryBrazil
CityCuritiba
Period11/8/0911/12/09

Keywords

  • Differential evolution
  • Electrical energy consumers
  • Fraud detection

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